Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications
Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications
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DOI：
10.1145/3292500.3330878
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Zhe Jiang;Arpan Man Sainju
中科院分区：
文献类型：
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作者：
Zhe Jiang;Arpan Man Sainju
空间结构化模型是预测模型，可根据其在空间中的位置捕获样品之间的依赖性结构。学习这样的模型在许多地球科学应用中起着重要的作用，例如水面映射，但由于连续空间和高计算成本的隐式依赖结构，这也构成了重大挑战。现有模型通常假定依赖性结构基于空间接近度或网络拓扑，因此无法在3D电位表面上融合复杂的依赖性结构，例如轮廓和流动方向。为了填补空白，本文提出了一个新型的空间结构模型，称为“隐藏的马尔可夫轮廓树”（HMCT），该模型将传统的隐藏马尔可夫模型从总顺序序列概括为部分顺序polytree。 HMCT还通过在3D表面上捕获复杂的轮廓结构，在隐藏的马尔可夫树上进行现有工作。我们提出有效的模型构建和学习算法。对现实世界水文数据集的评估表明，我们的HMCT在分类性能中的多种基线方法优于分类性能的多种基线方法，并且HMCT可扩展到大数据大小（例如，在几秒钟内对数百万个样本进行分类）。
Spatial structured models are predictive models that capture dependency structure between samples based on their locations in the space. Learning such models plays an important role in many geoscience applications such as water surface mapping, but it also poses significant challenges due to implicit dependency structure in continuous space and high computational costs. Existing models often assume that the dependency structure is based on either spatial proximity or network topology, and thus cannot incorporate complex dependency structure such as contour and flow direction on a 3D potential surface. To fill the gap, this paper proposes a novel spatial structured model called hidden Markov contour tree (HMCT), which generalizes the traditional hidden Markov model from a total order sequence to a partial order polytree. HMCT also advances existing work on hidden Markov trees through capturing complex contour structures on a 3D surface. We propose efficient model construction and learning algorithms. Evaluations on real world hydrological datasets show that our HMCT outperforms multiple baseline methods in classification performance and that HMCT is scalable to large data sizes (e.g., classifying millions of samples in seconds).